Fall, 2013 -- Course Information Sheet -- RESCH-GE.2003
Instructor: Sharon L. Weinberg
Email: [email protected] Phone/Fax: 212-998-2373/212-995-4832
Office: 805 Kimball Hall, 246 Greene Street
Office Hours: Tuesdays, 10 am to noon and by appointment
TA: Sukhmani Singh
Email: [email protected]
Office Hours: TBD
Prerequisites: RESCH-GE.2001-2002 or the equivalent
Meeting Times/Location: Tuesdays, 3:30 pm to 6:10 pm in GCASL Room 365
Lab Section Meeting Times/Rooms: Attendance in lab is strongly recommended and
encouraged. Lab meets on Thursdays from 3:30 pm to 4:45 pm in Tisch, LC19. The lab
provides additional SPSS and STATA demonstrations of what is discussed in class, and
hands-on guidance for homework assignments.
Course Goals: This course extends the material covered in RESCH-GE.2001-2 by
examining more deeply multiple regression/correlation as a general and flexible system
for analyzing data in the behavioral, social, and health sciences. In addition to covering
more advanced topics related to traditional multiple regression/correlation, the course
examines ANOVA and ANCOVA as special cases of the general linear model. Logistic
regression also is covered. Logistic regression does not fall under the general linear
model heading as it applies when the dependent variable is categorical, not
continuous. The software packages, SPSS and STATA, are used to give students handson experience with topics covered. The course aims to provide graduate students with
foundational skills and knowledge for analyzing quantitative data.
Course Orientation: A conceptually oriented, nonmathematical approach is used. The
course is not appropriate for students seeking to learn the mathematical theory
underlying these techniques.
Course Materials:
Website: Handouts, lecture notes, readings, homework assignments, and general
information will be posted under Resources on our My Classes course website. Lecture
notes will be posted on our class website for each lecture under Resources. You are
advised to download these notes prior to each lecture and bring a copy of them to class,
either as hard or soft copies to facilitate note taking. Along with lecture notes, all data
sets used in a lecture also will be posted under Resources so that you may review and
replicate on your own whatever analyses have been carried out in class.
Texts: The course lecture notes serve as the primary text for the course; the following
textbooks, available at the NYU Bookstore, are useful as an additional source of
information and future reference:
Cohen, P., Cohen, J., West, S.G., & Aiken, L.S. Applied Multiple Regression/Correlation
Analysis for the Behavioral Sciences, 3rd edition, Lawrence Erlbaum Associates. [C]
Warner, R. M. Applied Statistics: From Bivariate Through Multivariate Techniques, 2nd
edition, Sage Publications, Inc. [W]
Computer Labs (Virtual and Actual):
NYU offers a Virtual Computer Lab (VCL) to all NYU degree-seeking students with active email accounts. Students who qualify will see the VCL channel on the Academics tab in
NYUHome. To access the VCL: Log into NYUHome (home.nyu.edu); Select the Academics
tab, then scroll down until you see the "Virtual Computer Lab" channel; Click VCL Log In;
Once on the VCL page, click Log Into the VCL Now!; Enter your NetID and password; Click
Log In. Note: The first time you log into the VCL, you will be prompted to install the Citrix
ICA plug-in.
Both SPSS (version 20) and STATA (version 11) are available through the VCL, and also
through the Actual Computer Labs.
SPSS version 20 is available at the following Actual Computer Labs:
Kimball Hall, 3E (Windows) [This lab does not require swiping your ID]
Fourth Street Academic Technology Center (Mac and Windows)
Washington Place Technology Center (Mac and Windows)
Third Avenue (Mac and Windows)
Kimmel Center (Mac and Windows)
STATA (version 11) is available at all of the above computer labs, but for Kimball Hall, and a
Mac version is not available at Third Avenue.
For a current list of software available by location, please see the ITS Software Applications by Location page. Course Requirements & Grading:
Homework: Practicing what has been covered in class is essential to learning statistics.
HW will be assigned, collected, and graded each week. All students are responsible for
completing all homework assignments on time and raising related questions in class.
Grading:
10% Class attendance and participation
90% Weekly computer-based homework sets (approx. ten in all)
Syllabus:
FALL, 2013 -­‐-­‐ INTERMEDIATE QUANTITATIVE METHODS -­‐-­‐ RESCH-­‐GE.2003 Month Day Topic Reading (C & W) September 3 10 24 October 1 8 15 22 November 5 12 19 26 The k Predictor Case: Model Building
Strategies: Simultaneous, Hierarchical, and
Stepwise Approaches; Statistical Inference in
Multiple Regression
Nonlinear Transformations and Regression
Diagnostics -- Checking and Addressing
Underlying Assumptions
3 10 C -- pp. 2 – 36;
W – ch. 1 - 9
C -- pp. 37-62;
W -- ch. 10 & 11
C -- pp. 64-99
W – ch.14
C -- pp. 101-150
Critiquing an article that uses MR
Okazaki, 1997 (posted on
our website)
Interactions -- The Case of a Dichotomous
and Quantitative Variable; the Case of Two
Quantitative Variables
C – pp. 255-300
W – ch.15
MIDTERM BREAK-­‐-­‐NO CLASS Post Hoc Probing of Interactions (using
SPSS MODPROBE & STATA commands)
C – pp. 255-300
Mediation (using SPSS INDIRECT); Intro to
Path Analysis
Baron & Kenny, 1986;
Preacher & Hayes, 2004
(posted on our website)
W -- ch. 16
C – pp. 301-342
W – ch..12
29 December 17 Statistical Procedures: A Conceptual Map;
Univariate & Bivariate Statistics -- A Review
Statistical Control: The Two-Predictor Case
From Single Predictors to Sets of Predictors:
Qualitative Scales using Dummy Coding
(Indicator Variables), Quantitative Scales,
Analytic Strategies, Proportion of VAF, Tests
of Inference
Multiple Regression as a General Linear
Model -- ANOVA as a Special Case of GLM
C – pp. 343-389
W – ch.13
Multiple Regression as a General Linear
Model -- ANCOVA as a Special Case of
GLM; Lord's Paradox
When the DV is logged; Power Polynomials
C – pp. 343-389
W – ch. 17
Logistic Regression -- When the DV is
Dichotomous (Generalized Linear Equations)
C -- pp. 479-519
W – ch.23
Logistic Regression, Cont'd; Characterizing
Differences among Methods Covered;
Wrapping Up.
C – pp. 479-519
W – ch. 23
C – pp. 193-214